Identifying process gaps · Quantifying business impact · Delivering intelligent automation. A structured, evidence-based approach that closes operational gaps with purposeful Agentic AI and RAG — not a generic AI overlay.
Across every enterprise we engage with, eight operational areas share the same root pattern: manual handoffs, disconnected data, and decision-making that doesn't scale. Conventional improvement addresses symptoms — we address structural root causes with purposeful Agentic AI and RAG.
Manual code reviews, repetitive legacy bug-fixing, and unstructured AI tooling consume the most expensive engineering hours and slow every release.
Partial regression runs, manual test suites, and CI/CD pipelines without automated quality gates allow defects to leak into production every release.
Daily revenue reconciliation across time-granularities and dimensions is manual, slow, and reactive — drops surface only after they have already cost real money.
Daily sales across managers, agencies, products, and categories reviewed manually — pricing and promotion decisions lag, business users wait behind data engineers.
Enterprise data scattered across DB2, Oracle, Databricks, Snowflake, lakes, and object stores — every business question becomes a new engineering backlog item.
Recurring incidents missed, root causes never categorised, and resolution knowledge buried in TSE phone calls — every escalation gets investigated from scratch.
CV reviews, video interview assessments, and proctoring done manually — inconsistent rubrics, no audit trail, and a hard ceiling on candidate throughput.
Sentiment and emotion signals across thousands of interactions reviewed manually, if at all — churn risk surfaces too late to act, CX strategy runs on intuition.
A structured, repeatable five-step assessment cycle — every recommendation grounded in measurable business impact before a single line of code is written.
Structured assessment across targeted departments & stakeholders.
Process mining, interviews & end-to-end workflow analysis.
Bottlenecks, redundant handoffs & manual decision points exposed.
Cycle time, error rate, headcount cost & revenue leakage.
Opportunity register ranked by effort-to-value ratio.
Identified gaps are closed using Agentic AI and RAG as the core intelligence layer, supported by LLMs, ML models, RPA, and deep API integrations. Agentic AI orchestrates end-to-end workflows autonomously; RAG ensures every AI response is grounded in real enterprise data.
Autonomous AI agents that plan, reason, and execute multi-step workflows — taking actions, calling tools, and self-correcting without manual intervention.
Grounds AI responses in your actual enterprise data — retrieves relevant documents, records, and knowledge to deliver accurate, context-aware answers.
Unstructured data processing, document understanding, and natural language interfaces over your business systems.
Predictive decision-making, forecasting, and anomaly detection across business data — embedded inside agent workflows.
System-level task execution and integration across enterprise applications — including portals without APIs.
Connecting disparate enterprise systems into unified, automated workflows — REST, webhooks, and native connectors.
Concrete patterns we deploy across our eight focused business areas. RAG provides the data grounding; Agentic AI provides the autonomous execution. Real client engagements behind every pattern — drill in via the case studies page.
Grounds every review and fix in your existing codebase, internal coding standards, architectural patterns, and the history of past PR comments — recommendations anchored in your engineering org's conventions.
Reviews every PR/MR autonomously within minutes, raises root-cause patches for production bugs against legacy code, and codifies AI-engineering practice itself — humans review final output instead of hunting through diffs.
Indexes existing test cases, user stories, acceptance criteria, and historical defect patterns — every generated test grounds against established intent and known failure modes.
Generates and maintains test suites for new and existing modules, drives execution through custom Playwright MCP servers, and gates Jenkins releases on the result — no merge without 100% regression coverage.
Stores revenue records, pricing history, campaign attributes, and market signals in a PostgreSQL pgvector RAG — every analysis runs against grounded historical context, not just the last 24 hours.
Reconciles daily revenue across every time-cut and dimension, alerts on drops and gains within hours of day-boundary, and queues corrective campaign actions before office hours — the analyst reviews recommendations, not reports.
Stores sales records by manager, agency, product, and category — with pricing history, promotion outcomes, and combo performance — providing grounded context for every sales pattern and forecast.
Surfaces daily sales drops and gains across every dimension, autonomously queues product, discount, and combo actions for the next day, and exposes a chatbot so sales leaders get answers in plain English.
Crawls and indexes metadata across every data source — DB2, Oracle, Databricks, Snowflake, ADLS, S3, lakehouses — with business rules layered on top for grounded retrieval and self-service discovery.
Maps natural-language questions to underlying metadata, builds federated queries across sources, executes via a distributed query engine, and assembles complete data products — replacing data-engineering ticket queues with a single chat surface.
Distils support tickets, MS Teams call transcripts, and TSE conversations into a structured, searchable knowledge base linked to underlying tickets — every escalation gets context from organisational memory.
Analyses tickets at scale, auto-categorises root causes, identifies systemic patterns, and generates actionable user stories with effort estimates and roadmaps — turning ticket noise into prioritised engineering signal.
Grounds every candidate evaluation in your weighted skills framework, JD requirements, hiring rubrics, and prior decisions — every score traces back to your own criteria, not an opaque model judgement.
Auto-scores CVs against role requirements, evaluates Claude-Vision video assessments on five dimensions, flags integrity incidents with timestamped evidence, and produces Hire / Further Evaluate / Reject calls with full audit trail.
Indexes 12+ months of customer interactions across tickets, chat, and email — sentiment trends, emotion patterns, and churn signals all grounded in actual customer-journey context, not point-in-time snapshots.
Scores every interaction across sentiment, emotion, urgency, and churn-risk dimensions; alerts on early-warning churn patterns; and produces full-year CX dashboards with actionable signal — replacing intuition with measured outcomes.
Quick wins delivered early; long-term intelligent operations capability built progressively, phase by phase. Monitoring, fixes, and continuous improvement embedded throughout delivery.
Identify opportunities. Quantify business impact. Build the case.
Architecture, model selection, success metrics & proof of concept.
Production-grade build with end-to-end integration & QA.
Roll out, enable adoption, monitor & provide post-launch support.
Five reasons enterprises choose SharabhaTech over generic AI consulting or in-house experimentation.
Every recommendation grounded in quantified business impact — cycle time, cost, error rate, and revenue leakage. We measure before we propose.
Agentic AI and RAG applied specifically to identified process gaps — not a generic AI overlay sprinkled on top of unchanged operations.
Eight focused business areas — Developer Productivity, Production Reliability, Revenue Management, Sales Operations, Data Operations, Customer Support, Talent Acquisition, and Customer Experience — full vertical depth, not horizontal-only platform plays.
Quick wins delivered early; long-term intelligent operations capability built progressively, phase by phase. Value compounds; risk stays bounded.
Monitoring, fixes, oversight, and exception escalation paths embedded from day one. Agents never operate beyond defined boundaries.